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Record W3165346424 · doi:10.1161/jaha.120.019051

Impact of Morbid Obesity and Obesity Phenotype on Outcomes After Transcatheter Aortic Valve Replacement

2021· article· en· W3165346424 on OpenAlexaff
Angela McInerney, Gabriela Tirado‐Conte, Josep Rodés‐Cabau, Francisco Campelo‐Parada, José D. Tafur Soto, Marco Barbanti, Érika Muñoz-García, Mobeena Arif, Diego Lopez, Stefan Toggweiler, Gabriela Veiga Fernández, Anna Pyłko, Teresa Sevilla, Miriam Compagnone, Ander Regueiro, Viçent Serra, Manuel Carnero, J. Domínguez, Fernando Rivero, Henrique Barbosa Ribeiro, Leonardo Guimarães, Anthony Matta, Natalia Giraldo Echavarría, Roberto Valvo, Federico Moccetti, Antonio J. Muñoz-García, Javier López‐País, Bruno García del Blanco, Diego Carter Campanha Borges, Éric Dumont, Nieves Gonzalo, Enrico Criscione, Maciej Dąbrowski, Fernándo Alfonso, José M. de la Torre Hernández, Asim N. Cheema, Ignacio J. Amat‐Santos, Francesco Saia, Javier Escaned, Luis Nombela‐Franco

Bibliographic record

VenueJournal of the American Heart Association · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Michael's HospitalUniversité LavalMontreal Heart Institute
FundersFundación Interhospitalaria para la Investigación CardiovascularBoston Scientific CorporationEdwards Lifesciences
KeywordsMedicineHazard ratioInternal medicineBody mass indexAdipose tissueAortic valve replacementCardiologyObesityValve replacementCohortSurgeryGastroenterologyStenosisConfidence interval

Abstract

fetched live from OpenAlex

Background There is a paucity of outcome data on patients who are morbidly obese (MO) undergoing transcatheter aortic valve replacement. We aimed to determine their periprocedural and midterm outcomes and investigate the impact of obesity phenotype. Methods and Results Consecutive patients who are MO (body mass index, ≥40 kg/m 2 , or ≥35 kg/m 2 with obesity‐related comorbidities; n=910) with severe aortic stenosis who underwent transcatheter aortic valve replacement in 18 tertiary hospitals were compared with a nonobese cohort (body mass index, 18.5–29.9 kg/m 2 , n=2264). Propensity‐score matching resulted in 770 pairs. Pre–transcatheter aortic valve replacement computed tomography scans were centrally analyzed to assess adipose tissue distribution; epicardial, abdominal visceral and subcutaneous fat. Major vascular complications were more common (6.6% versus 4.3%; P =0.043) and device success was less frequent (84.4% versus 88.1%; P =0.038) in the MO group. Freedom from all‐cause and cardiovascular mortality were similar at 2 years (79.4 versus 80.6%, P =0.731; and 88.7 versus 87.4%, P =0.699; MO and nonobese, respectively). Multivariable analysis identified baseline glomerular filtration rate and nontransfemoral access as independent predictors of 2‐year mortality in the MO group. An adverse MO phenotype with an abdominal visceral adipose tissue:subcutaneous adipose tissue ratio ≥1 (VAT:SAT) was associated with increased 2‐year all‐cause (hazard ratio [HR], 3.06; 95% CI, 1.20–7.77; P =0.019) and cardiovascular (hazard ratio, 4.11; 95% CI, 1.06–15.90; P =0.041) mortality, and readmissions (HR, 1.81; 95% CI, 1.07–3.07; P =0.027). After multivariable analysis, a (VAT:SAT) ratio ≥1 remained a strong predictor of 2‐year mortality (hazard ratio, 2.78; P =0.035). Conclusions Transcatheter aortic valve replacement in patients who are MO has similar short‐ and midterm outcomes to nonobese patients, despite higher major vascular complications and lower device success. An abdominal VAT:SAT ratio ≥1 identifies an obesity phenotype at higher risk of adverse clinical outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.331
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2021
Admission routes1
Has abstractyes

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